This role pays more than
86%
of similar roles. Most pay
$165,250–$215,175
— the shaded band above.
At the midpoint, this role pays about
$232k
versus about
$190k
for comparable roles.
Based on 239 similar postings.
Employer
About Uber
Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.
Uber currently has
95 open roles
on FindRole.
Listed pay typically runs
$232,000–$232,000
across 76 roles with salary data.
As a Staff Engineer in Core Infrastructure, you will lead the architectural evolution and development of backend solutions that power Uber’s global operations, focusing on creating a secure, reliable, and hyperscale-efficient cloud-native ecosystem. You will work with stakeholders to design systems capable of handling 1M+ concurrent trips and Generative AI data demands. Key responsibilities include driving efficiency at scale by optimizing CPU utilization and ARM adoption, modernizing for multi-cloud resilience, integrating AIOps tools, championing security practices, and collaborating across teams to support large-scale models and autonomous vehicle data ingestion. The role requires expertise in Go, Java, or C++, distributed systems design, Kubernetes internals, and AI/ML infrastructure, with a focus on hybrid-cloud migrations and cost optimization. You will also mentor engineers and foster operational excellence within the team that builds Uber’s next-generation orchestration and networking layers.
Linux
C++
C
Camera HAL
Linux Kernel
Device Driver Model
MIPI
GMSL
I2C
GPIOs
3A Algorithms
V4L2
OpenCL
OpenGL
Android Camera Frameworks
Multimedia Software Development
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AWS
GCP
Azure
Java
Scala
Python
SQL
NoSQL
Spark
Kafka
API-first
microservices
ETL
real-time data processing
big data frameworks
AI-driven automation
agent-based systems
cloud-native development
linear advertising
C++
Python
Linux
Android
QNX
Machine Learning
AI Models
Hardware Architecture
Compiler Tools
Model Efficiency Tools
Inference Solutions
Training Solutions
Experimentation
Evaluation Techniques